# migueldeicaza/TensorFlowSharp

TensorFlow API for .NET languages

Repository: https://github.com/migueldeicaza/TensorFlowSharp
Canonical: https://ross.abutalabs.com/products/tensorflowsharp
Language: C#
License: MIT
License Family: permissive
Topics: dot-net, machine-learning, tensorflow, mono, xamarin, f-sharp, c-sharp
Archived: true
Last push: 2024-01-12T14:41:05+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3516, "days_push": 964, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3147, forks 562 (observed 2026-08-28T04:07:46.038829+00:00)

## What it is
TensorFlowSharp provides .NET bindings to the TensorFlow C API, exposing a strongly-typed low-level API for C# and F#. It is designed mainly for running pre-trained TensorFlow models in .NET and is no longer actively maintained in favor of TensorFlow.NET.

## Use cases
- run a trained tensorflow model from c#
- load a saved tensorflow graph in a .net application
- use tensorflow from f#
- execute inference with tensorflow on mono or xamarin
- prototype in python keras then run the model in .net

## When to choose
- you need to run existing TensorFlow models from C# or F# with a low-level graph/session API
- you are on a legacy .NET/Mono/Xamarin stack and cannot migrate

## When to avoid
- you want high-level training APIs or active maintenance - use TensorFlow.NET instead
- you are starting a new machine-learning project in .NET

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: dotnet, windows, cross-platform
- tags: tensorflow, bindings, nuget, mono, xamarin, deprecated, f-sharp, linux, macos

## Member repositories
- migueldeicaza/TensorFlowSharp (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:46.038829+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:25:56.703137+00:00, confidence not recorded.
  - readme: https://github.com/migueldeicaza/TensorFlowSharp (fetched 2026-08-28T04:07:46.038829+00:00, sha 34d7b76ce048)
- Data as of 2026-08-30T08:39:29.467469+00:00.